Legionnaires’ Disease Outbreaks
Holly Kessler
August 10, 2016
The coordinates of the Legionnairess Disease outbreaks will be used to gather weather data from surrounding stations. The averages of the data will be taken and outputted into a graph containing data from the last 10 years before the outbreak.
library(devtools)
library(rnoaa)
library(countyweather)
library(dplyr)
library(plyr)
library(tidyr)
library(weathermetrics)
library(ggplot2)
library(lubridate)
library(knitr)I created a data frame including the locations of each outbreak. I found the coordinates at http://maps.cga.harvard.edu/gpf/ and crossed checked them with Google coordinates. The other data in this set are year of outbreak and the year 10 years before the outbreak, onset date, and 14 days before the onset date.
## id file_id latitude longitude year_min
## 1 portugal portugal 38.96 -8.99 2004
## 2 pittsburgh pittsburgh 40.43 -79.98 2002
## 3 quebec quebec 46.85 -71.34 2002
## 4 stoke-on-trent stoke_on_trent 53.02 -2.15 2002
## 5 edinburgh edinburgh 55.94 -3.20 2002
## 6 miyazaki miyazaki 31.89 131.34 1992
## 7 pas-de-calais pas_de_calais 50.51 2.37 1993
## 8 pamplona pamplona 42.81 -1.65 1996
## 9 rapid city rapid_city 44.06 -103.22 1995
## 10 sarpsborg sarpsborg 59.28 11.08 1995
## 11 barrow-in-furness barrow_in_furness 54.10 -3.22 1992
## 12 murcia murcia 37.98 -1.12 1991
## 13 melbourne melbourne -37.86 145.07 1990
## 14 bovenkarspel bovenkarspel 52.70 5.24 1989
## 15 london london 51.52 -0.10 1979
## 16 sydney sydney -33.85 150.93 2006
## 17 genesee1 genesee1 43.09 -83.63 2004
## 18 genesee2 genesee2 43.09 -83.63 2005
## 19 columbus columbus 39.98 -82.99 2003
## 20 bronx bronx 40.82 -73.92 2005
## date_min year_max date_max onset before_onset
## 1 2004-01-01 2014 2014-12-31 2004-10-14 2004-09-30
## 2 2002-01-01 2012 2012-12-31 2012-08-26 2012-08-12
## 3 2002-01-01 2012 2012-12-31 2012-07-18 2012-07-04
## 4 2002-01-01 2012 2012-12-31 2012-07-02 2012-06-18
## 5 2002-01-01 2012 2012-12-31 2012-05-01 2012-04-17
## 6 1992-01-01 2002 2002-12-31 2002-07-18 2002-07-04
## 7 1993-01-01 2003 2003-12-31 2003-11-28 2003-11-14
## 8 1996-01-01 2006 2006-12-31 2006-06-01 2006-05-18
## 9 1995-01-01 2005 2005-12-31 2005-05-26 2005-05-12
## 10 1995-01-01 2005 2005-12-31 2005-05-12 2005-04-28
## 11 1992-01-01 2002 2002-12-31 2002-07-30 2002-07-16
## 12 1991-01-01 2001 2001-12-31 2001-06-26 2001-06-12
## 13 1990-01-01 2000 2000-12-31 2000-04-17 2000-04-03
## 14 1989-01-01 1999 1999-12-31 1999-02-25 1999-02-11
## 15 1979-01-01 1989 1989-12-31 1989-01-01 1988-12-18
## 16 2006-01-01 2016 2016-12-31 2016-04-25 2016-04-11
## 17 2004-01-01 2014 2014-12-31 2014-06-06 2014-05-23
## 18 2005-01-01 2015 2015-12-31 2015-05-04 2015-04-20
## 19 2003-01-01 2013 2013-12-31 2013-07-09 2013-06-25
## 20 2005-01-01 2015 2015-12-31 2015-07-12 2015-06-28| id | file_id | latitude | longitude | year_min | date_min | year_max | date_max | onset | before_onset |
|---|---|---|---|---|---|---|---|---|---|
| portugal | portugal | 38.96 | -8.99 | 2004 | 2004-01-01 | 2014 | 2014-12-31 | 2004-10-14 | 2004-09-30 |
| pittsburgh | pittsburgh | 40.43 | -79.98 | 2002 | 2002-01-01 | 2012 | 2012-12-31 | 2012-08-26 | 2012-08-12 |
| quebec | quebec | 46.85 | -71.34 | 2002 | 2002-01-01 | 2012 | 2012-12-31 | 2012-07-18 | 2012-07-04 |
| stoke-on-trent | stoke_on_trent | 53.02 | -2.15 | 2002 | 2002-01-01 | 2012 | 2012-12-31 | 2012-07-02 | 2012-06-18 |
| edinburgh | edinburgh | 55.94 | -3.20 | 2002 | 2002-01-01 | 2012 | 2012-12-31 | 2012-05-01 | 2012-04-17 |
| miyazaki | miyazaki | 31.89 | 131.34 | 1992 | 1992-01-01 | 2002 | 2002-12-31 | 2002-07-18 | 2002-07-04 |
| pas-de-calais | pas_de_calais | 50.51 | 2.37 | 1993 | 1993-01-01 | 2003 | 2003-12-31 | 2003-11-28 | 2003-11-14 |
| pamplona | pamplona | 42.81 | -1.65 | 1996 | 1996-01-01 | 2006 | 2006-12-31 | 2006-06-01 | 2006-05-18 |
| rapid city | rapid_city | 44.06 | -103.22 | 1995 | 1995-01-01 | 2005 | 2005-12-31 | 2005-05-26 | 2005-05-12 |
| sarpsborg | sarpsborg | 59.28 | 11.08 | 1995 | 1995-01-01 | 2005 | 2005-12-31 | 2005-05-12 | 2005-04-28 |
| barrow-in-furness | barrow_in_furness | 54.10 | -3.22 | 1992 | 1992-01-01 | 2002 | 2002-12-31 | 2002-07-30 | 2002-07-16 |
| murcia | murcia | 37.98 | -1.12 | 1991 | 1991-01-01 | 2001 | 2001-12-31 | 2001-06-26 | 2001-06-12 |
| melbourne | melbourne | -37.86 | 145.07 | 1990 | 1990-01-01 | 2000 | 2000-12-31 | 2000-04-17 | 2000-04-03 |
| bovenkarspel | bovenkarspel | 52.70 | 5.24 | 1989 | 1989-01-01 | 1999 | 1999-12-31 | 1999-02-25 | 1999-02-11 |
| london | london | 51.52 | -0.10 | 1979 | 1979-01-01 | 1989 | 1989-12-31 | 1989-01-01 | 1988-12-18 |
| sydney | sydney | -33.85 | 150.93 | 2006 | 2006-01-01 | 2016 | 2016-12-31 | 2016-04-25 | 2016-04-11 |
| genesee1 | genesee1 | 43.09 | -83.63 | 2004 | 2004-01-01 | 2014 | 2014-12-31 | 2014-06-06 | 2014-05-23 |
| genesee2 | genesee2 | 43.09 | -83.63 | 2005 | 2005-01-01 | 2015 | 2015-12-31 | 2015-05-04 | 2015-04-20 |
| columbus | columbus | 39.98 | -82.99 | 2003 | 2003-01-01 | 2013 | 2013-12-31 | 2013-07-09 | 2013-06-25 |
| bronx | bronx | 40.82 | -73.92 | 2005 | 2005-01-01 | 2015 | 2015-12-31 | 2015-07-12 | 2015-06-28 |
The next function will download information from all of the stations. It only needs to be downloaded once per session. It will take a couple minutes to download.
I created a loop to get a list of the stations within 30 km of the location.
station_data <- ghcnd_stations()[[1]]
df <- list()
for(i in 1:length(outbreak_loc$id))
{
df[[i]] <- (meteo_nearby_stations(lat_lon_df = outbreak_loc[i,],
station_data = station_data,
var = c("PRCP","TAVG","TMAX","TMIN",
"AWND","MDPR"),
year_min = outbreak_loc[i, "year_min"],
year_max = outbreak_loc[i, "year_max"],
radius = 30)[[1]])
}
names(df) <- outbreak_loc$id
stations <- df
saveRDS(stations, file = "stations.RData")## $portugal
## [1] id name latitude longitude distance
## <0 rows> (or 0-length row.names)
##
## $pittsburgh
## id name latitude longitude distance
## 1 US1PAAL0014 PA PITTSBURGH 1.6 SW 40.4226 -79.9974 1.687108
## 2 US1PAAL0017 PA WHITEHALL 1.0 SW 40.3475 -80.0022 9.364279
## 3 USW00014762 PA PITTSBURGH ALLEGHENY CO AP 40.3547 -79.9217 9.720301
## 4 US1PAAL0011 PA WEST MIFFLIN 1.3 SW 40.3466 -79.9283 10.255413
## 5 US1PAAL0031 PA SCOTT TOWNSHIP 1.3 NW 40.3978 -80.0967 10.508788
## 6 USC00360861 PA BRADDOCK LOCK 2 40.3917 -79.8594 11.063215
## 7 US1PAAL0009 PA PATHFINDER 40.3416 -80.0485 11.414110
## 8 USC00362574 PA EMSWORTH L/D OHIO RVR 40.5019 -80.0833 11.844197
## 9 US1PAAL0016 PA GLENSHAW 1.3 NW 40.5488 -79.9800 13.209957
## 10 USC00365573 PA MCKEESPORT 40.3392 -79.8603 14.308275
## 11 US1PAAL0008 PA UPPER ST. CLAIR 1.7 WNW 40.3412 -80.1026 14.329100
## 12 US1PAAL0020 PA ALLISON PARK 0.7 W 40.5610 -79.9708 14.587294
## 13 US1PAAL0023 PA SOUTH PARK TOWNSHIP 0.2 NW 40.2989 -79.9970 14.648635
## 14 US1PAAL0003 PA SOUTH FAYETTE 2 SE 40.3381 -80.1159 15.392161
## 15 US1PAAL0001 PA BRIDGEVILLE 1.4 SW 40.3417 -80.1229 15.584945
## 16 US1PAAL0004 PA PENN HILLS 1.5 E 40.4759 -79.7982 16.207171
## 17 USC00360022 PA ACMETONIA LOCK 3 40.5361 -79.8153 18.254228
## 18 US1PAAL0006 PA MCDONALD 2.5 ENE 40.3822 -80.1871 18.323293
## 19 US1PAWS0005 PA MCMURRAY 0.2 NE 40.2831 -80.0857 18.628831
## 20 USW00094823 PA PITTSBURGH INTL AP 40.4847 -80.2144 20.743637
## 21 US1PAAL0030 PA CARNOT-MOON 0.9 S 40.5061 -80.2119 21.364459
## 22 USC00366111 PA MURRYSVILLE 2 SW 40.4119 -79.7244 21.730660
## 23 US1PAWT0001 PA NORTH IRWIN 2.5 WSW 40.3243 -79.7556 22.348628
## 24 USC00365918 PA MOON TOWNSHIP 40.5319 -80.2172 23.040372
## 25 USC00363343 PA GLENWILLARD DASHIELDS 40.5514 -80.2167 24.143027
## 26 US1PAWT0010 PA MURRYSVILLE 1.5 WSW 40.4317 -79.6813 25.282776
## 27 US1PAAL0012 PA SOUTH HEIGHTS 1.5 S 40.5533 -80.2379 25.760534
##
## $quebec
## id name latitude longitude distance
## 1 CA007011309 QC CHARLESBOURG PARC ORLEAN 46.8667 -71.2667 5.874619
## 2 CA007016294 QC QUEBEC/JEAN LESAGE INTL A 46.8000 -71.3833 6.462488
## 3 CA00701S001 QC QUEBEC/JEAN LESAGE INTL 46.8000 -71.3833 6.462488
## 4 CA00701Q004 QC STE-FOY (U. LAVAL) 46.7833 -71.2833 8.580380
## 5 CA007010565 QC BEAUPORT 46.8333 -71.2000 10.808994
## 6 CA007018572 QC VALCARTIER 46.9000 -71.5000 13.372471
## 7 CA007024254 QC LAUZON 46.8167 -71.1000 18.628731
## 8 CA007020567 QC BEAUSEJOUR 46.6667 -71.1667 24.283856
## 9 CA007041330 QC CHATEAU RICHER 46.9667 -71.0333 26.668362
##
## $`stoke-on-trent`
## [1] id name latitude longitude distance
## <0 rows> (or 0-length row.names)
##
## $edinburgh
## [1] id name latitude longitude distance
## <0 rows> (or 0-length row.names)
##
## $miyazaki
## id name latitude longitude distance
## 1 JA000047830 MIYAZAKI 31.933 131.417 8.699733
## 2 JA000047829 MIYAKONOJO 31.733 131.083 29.908209
##
## $`pas-de-calais`
## [1] id name latitude longitude distance
## <0 rows> (or 0-length row.names)
##
## $pamplona
## id name latitude longitude distance
## 1 SPE00120350 PAMPLONA (OBSERVATORIO) 42.8175 -1.6364 1.387848
## 2 SPE00120359 PAMPLONA 42.7767 -1.6500 3.702791
##
## $`rapid city`
## id name latitude longitude distance
## 1 USC00396948 SD RAPID CITY WFO 44.0728 -103.2108 1.601898
## 2 USC00396947 SD RAPID CITY 4NW 44.1150 -103.2828 7.909484
## 3 USW00024090 SD RAPID CITY RGNL AP 44.0433 -103.0536 13.427258
## 4 USC00394343 SD JOHNSON SIDING 44.0839 -103.4342 17.317536
## 5 USR0000SBAK SD BAKER PARK SOUTH DAKOTA 43.9792 -103.4250 18.692682
## 6 USC00396427 SD PACTOLA DAM 44.0622 -103.4819 20.928415
## 7 USC00394556 SD KEYSTONE 43.9039 -103.4100 23.073539
## 8 USR0000SNEM SD NEMO SOUTH DAKOTA 44.1917 -103.5097 27.370234
## 9 USC00395870 SD MT RUSHMORE NATL MEM 43.8769 -103.4578 27.869317
## 10 USR0000SMRU SD MT. RUSHMORE SOUTH DAKOTA 43.8750 -103.4583 28.051427
## 11 USC00393775 SD HERMOSA 3 SSW 43.8069 -103.2131 28.148859
##
## $sarpsborg
## id name latitude longitude distance
## 1 NOE00109849 SARPSBORG 59.2856 11.1144 2.050694
## 2 NOE00134298 FLOTER 59.4964 11.0131 24.358920
## 3 NOE00100575 HALDEN 59.1225 11.3883 24.795447
## 4 NOE00109786 HVALER 59.0358 11.0517 27.201683
## 5 NOE00109876 MOSS BRANNSTASJON 59.4428 10.6842 28.822789
## 6 NOE00109867 MOSS 59.4339 10.6667 29.008867
##
## $`barrow-in-furness`
## [1] id name latitude longitude distance
## <0 rows> (or 0-length row.names)
##
## $murcia
## id name latitude longitude distance
## 1 SPE00120323 MURCIA 38.0028 -1.1692 5.001690
## 2 SPE00120332 MURCIA/ALCANTARILLA 37.9578 -1.2294 9.902609
##
## $melbourne
## id name latitude longitude distance
## 1 ASN00086018 CAULFIELD (RACECOURSE) -37.8795 145.0368 3.632396
## 2 ASN00086304 HAWTHORN (SCOTCH COLLEGE) -37.8361 145.0294 4.446429
## 3 ASN00086095 PRAHRAN (COMO HOUSE) -37.8376 145.0048 6.243145
## 4 ASN00086088 OAKLEIGH (METROPOLITAN GOLF CL -37.9142 145.0935 6.369850
## 5 ASN00086012 BOX HILL AGED MENS RETREAT -37.8364 145.1364 6.393542
## 6 ASN00086006 BENTLEIGH -37.9279 145.0749 7.562369
## 7 ASN00086033 BRIGHTON (DENDY PARK BOWLING C -37.9252 145.0254 8.238821
## 8 ASN00086232 MELBOURNE BOTANICAL GARDENS -37.8303 144.9767 8.833034
## 9 ASN00086279 NORTHCOTE -37.7797 145.0314 9.551015
## 10 ASN00086316 VERMONT TRANSPORT RESEARCH -37.8587 145.1847 10.070617
## 11 ASN00086071 MELBOURNE REGIONAL OFFICE -37.8075 144.9700 10.545355
## 12 ASN00086020 CHELTENHAM KINGSTON CENTRE -37.9551 145.0782 10.599081
## 13 ASN00086303 GLEN WAVERLEY (GOLF COURSE) -37.8886 145.1928 11.237859
## 14 ASN00086074 MITCHAM -37.8219 145.1906 11.406147
## 15 ASN00086260 HEIDELBERG MMBW -37.7567 145.0533 11.579751
## 16 ASN00086378 BRUNSWICK -37.7667 144.9797 13.059611
## 17 ASN00086111 SPRINGVALE NECROPOLIS -37.9445 145.1764 13.245203
## 18 ASN00086369 SPRINGVALE (SANDOWN) -37.9535 145.1655 13.352636
## 19 ASN00086068 VIEWBANK (ARPANSA) -37.7408 145.0972 13.468157
## 20 ASN00086077 MOORABBIN AIRPORT -37.9800 145.0964 13.542852
## 21 ASN00086146 BEAUMARIS -37.9771 145.0273 13.548961
## 22 ASN00086362 DONCASTER (MANNINGHAM DEPOT) -37.7494 145.1703 15.129262
## 23 ASN00086351 BUNDOORA (LATROBE UNIVERSITY) -37.7163 145.0453 16.125457
## 24 ASN00086039 FLEMINGTON RACECOURSE -37.7915 144.9067 16.239778
## 25 ASN00086104 SCORESBY RESEARCH INSTITUTE -37.8710 145.2561 16.382188
## 26 ASN00086096 PRESTON RESERVOIR -37.7214 145.0059 16.408663
## 27 ASN00086230 BAYSWATER -37.8372 145.2558 16.509685
## 28 ASN00086379 RINGWOOD NORTH -37.7917 145.2433 17.010511
## 29 ASN00086101 RINGWOOD -37.8000 145.2500 17.158761
## 30 ASN00086313 WARRANDYTE -37.7469 145.2098 17.578887
## 31 ASN00086347 YARRA RIVER @ WARRANDYTE -37.7417 145.2167 18.416453
## 32 ASN00086224 DANDENONG -37.9785 145.2235 18.839479
## 33 ASN00086035 ELTHAM -37.7011 145.1547 19.172878
## 34 ASN00087038 MARIBYRNONG EXPLOSIVES FACTORY -37.7750 144.8767 19.432886
## 35 ASN00086324 FERNTREE GULLY (PROBERT) -37.8797 145.2964 19.993316
## 36 ASN00086027 CROYDON (SAMUEL STREET) -37.7903 145.2812 20.103933
## 37 ASN00086234 CROYDON (COUNCIL DEPOT) -37.7869 145.2847 20.535018
## 38 ASN00086038 ESSENDON AIRPORT -37.7276 144.9066 20.564229
## 39 ASN00087131 ALTONA (CITY OFFICES) -37.8633 144.8261 21.414594
## 40 ASN00087148 SUNSHINE (CITY OF BRINBANK) -37.7928 144.8344 22.000521
## 41 ASN00086250 PLENTY -37.6600 145.1244 22.747356
## 42 ASN00086256 FERNY CREEK -37.8833 145.3333 23.256162
## 43 ASN00086210 BONBEACH (CARRUM) -38.0651 145.1294 23.393049
## 44 ASN00086372 FERNY CREEK (DUNNS HILL) -37.8775 145.3364 23.465245
## 45 ASN00086365 MOOROOLBARK -37.7792 145.3197 23.701976
## 46 ASN00086251 UPWEY SHIRE COUNCIL -37.9144 145.3317 23.749365
## 47 ASN00086243 MOUNT DANDENONG GTV9 -37.8306 145.3500 24.802430
## 48 ASN00086059 KANGAROO GROUND -37.6830 145.2518 25.351527
## 49 ASN00086254 CARRUM DOWNS SEWER WORKS -38.0783 145.1733 25.907856
## 50 ASN00086036 EPPING -37.6312 144.9846 26.526359
## 51 ASN00086066 LILYDALE -37.7488 145.3416 26.875070
## 52 ASN00086076 MONTROSE -37.8019 145.3675 26.914609
## 53 ASN00086085 NARRE WARREN NORTH (NARRE WARR -37.9897 145.3356 27.399212
## 54 ASN00087031 LAVERTON RAAF -37.8565 144.7566 27.516720
## 55 ASN00087027 KEILOR (ARUNDEL) -37.6942 144.8342 27.737658
## 56 ASN00087177 LAVERTON COMPARISON -37.8633 144.7456 28.480727
## 57 ASN00086305 GREENVALE RESERVOIR -37.6369 144.9072 28.640881
## 58 ASN00086384 MELBOURNE AIRPORT COMPARISON -37.6750 144.8419 28.725743
## 59 ASN00087015 KEILOR -37.7025 144.8072 28.984935
##
## $bovenkarspel
## id name latitude longitude distance
## 1 NLE00101917 ENKHUIZEN 52.6917 5.2944 3.780363
## 2 NLE00109144 HOOGKARSPEL 52.6867 5.1669 5.143626
## 3 NLE00101928 MEDEMBLIK 52.7781 5.1014 12.746887
## 4 NLE00100501 HOORN 52.6444 5.0681 13.136277
## 5 NLE00102479 BERKHOUT 52.6428 4.9789 18.718869
## 6 NLE00109146 HOOGWOUD 52.7281 4.9608 19.065006
## 7 NLE00109174 KREILEROORD 52.8619 5.0953 20.464698
## 8 NLE00102134 STAVOREN 52.8967 5.3831 23.894441
## 9 NLE00109232 OBDAM 52.6775 4.8769 24.600539
## 10 NLE00109054 EDAM 52.5114 5.0467 24.701897
## 11 NLE00109250 OUDEMIRDUM 52.8608 5.5078 25.379621
## 12 NLE00109162 KOLHORN 52.7914 4.8919 25.540549
## 13 NLE00109354 WEST BEEMSTER 52.5817 4.9028 26.281190
## 14 NLE00101948 TOLLEBEEK 52.6719 5.6300 26.472774
## 15 NLE00101930 DEN OEVER 52.9217 5.0383 28.133541
## 16 NLE00101932 MARKEN 52.4600 5.1078 28.142012
## 17 NLE00109028 DE HAUKES 52.8783 4.9408 28.246661
## 18 NLE00109254 PURMEREND 52.5125 4.9506 28.575998
##
## $london
## id name latitude longitude distance
## 1 UKM00003772 HEATHROW 51.478 -0.461 25.42177
##
## $sydney
## id name latitude longitude distance
## 1 ASN00067019 PROSPECT RESERVOIR -33.8193 150.9127 3.769152
## 2 ASN00067017 GREYSTANES (BATHURST STREET) -33.8136 150.9392 4.135739
## 3 ASN00067070 MERRYLANDS (WELSFORD STREET) -33.8269 150.9767 5.020100
## 4 ASN00067114 ABBOTSBURY (FAIRFIELD CITY FAR -33.8667 150.8611 6.627566
## 5 ASN00067119 HORSLEY PARK EQUESTRIAN CENTRE -33.8511 150.8567 6.770114
## 6 ASN00067110 SEVEN HILLS (RADIO FM 103.2) -33.7858 150.9236 7.163157
## 7 ASN00067026 SEVEN HILLS (COLLINS ST) -33.7704 150.9318 8.852678
## 8 ASN00067020 LIVERPOOL (MICHAEL WENDEN CENT -33.9214 150.8861 8.913714
## 9 ASN00066137 BANKSTOWN AIRPORT AWS -33.9181 150.9864 9.189477
## 10 ASN00066134 GRANVILLE SHELL REFINERY -33.8322 151.0340 9.806921
## 11 ASN00066168 MILPERRA BRIDGE (GEORGES RIVER -33.9289 150.9831 10.049571
## 12 ASN00067042 KINGS LANGLEY (SOLANDER RD) -33.7610 150.9498 10.064021
## 13 ASN00067111 NORTH PARRAMATTA (BURNSIDE HOM -33.7931 151.0167 10.206744
## 14 ASN00067109 BAULKHAM HILLS EUCALYPTUS CT -33.7678 150.9814 10.300292
## 15 ASN00066124 PARRAMATTA NORTH (MASONS DRIVE -33.7917 151.0181 10.404864
## 16 ASN00066050 POTTS HILL RESERVOIR -33.8933 151.0346 10.790772
## 17 ASN00066164 ROOKWOOD (HAWTHORNE AVE) -33.8771 151.0577 12.169844
## 18 ASN00067112 NORTH ROCKS (MUIRFIELD GOLF CL -33.7672 151.0186 12.319787
## 19 ASN00066195 SYDNEY OLYMPIC PARK (SYDNEY OL -33.8521 151.0646 12.431978
## 20 ASN00066070 STRATHFIELD GOLF CLUB -33.8805 151.0631 12.748603
## 21 ASN00066054 REVESBY (PATEN STREET) -33.9474 151.0065 12.928587
## 22 ASN00067076 QUAKERS HILL TREATMENT WORKS -33.7366 150.8758 13.567795
## 23 ASN00066185 CARLINGFORD (BARELLAN AV) -33.7801 151.0587 14.205035
## 24 ASN00066191 GLENFIELD (HARROW ROAD) -33.9770 150.9042 14.321038
## 25 ASN00067117 HOLSWORTHY CONTROL RANGE -33.9795 150.9254 14.405998
## 26 ASN00067102 ST CLAIR (JUBA CLOSE) -33.8044 150.7778 14.945410
## 27 ASN00067100 CASTLE HILL (KATHLEEN AVE) -33.7260 150.9944 15.017779
## 28 ASN00067089 WEST PENNANT HILLS (CUMBERLAND -33.7459 151.0402 15.416884
## 29 ASN00067003 COLYTON (CARPENTER ST) -33.7770 150.7877 15.450666
## 30 ASN00067098 WEST PENNANT HILLS (ORATAVA A -33.7487 151.0449 15.478987
## 31 ASN00066013 CONCORD GOLF CLUB -33.8523 151.0985 15.562401
## 32 ASN00067061 ROSSMORE (SOUTH CREEK) -33.9353 150.7819 16.638119
## 33 ASN00066048 CONCORD (BRAYS RD) -33.8483 151.1105 16.669913
## 34 ASN00067037 SCHOFIELDS BOUNDARY RD -33.6947 150.8868 17.724215
## 35 ASN00066194 CANTERBURY RACECOURSE AWS -33.9057 151.1134 18.028240
## 36 ASN00066148 PEAKHURST GOLF CLUB -33.9700 151.0638 18.179759
## 37 ASN00066034 ABBOTSFORD (BLACKWALL POINT RD -33.8507 151.1295 18.423361
## 38 ASN00067116 WILLMOT (RESOLUTION AVE) -33.7231 150.7997 18.550317
## 39 ASN00066156 MACQUARIE PARK (WILLANDRA VILL -33.7791 151.1121 18.579019
## 40 ASN00066047 PENNANT HILLS (YARRARA ROAD) -33.7324 151.0767 18.835558
## 41 ASN00066190 INGLEBURN (SACKVILLE STREET) -34.0117 150.8647 18.962689
## 42 ASN00067108 BADGERYS CREEK AWS -33.8969 150.7281 19.355573
## 43 ASN00066181 OATLEY (WORONORA PARADE) -33.9766 151.0766 19.523783
## 44 ASN00066004 BEXLEY BOWLING CLUB -33.9430 151.1098 19.553323
## 45 ASN00067086 DURAL (OLD NORTHERN ROAD) -33.6867 151.0250 20.170027
## 46 ASN00066036 MARRICKVILLE GOLF CLUB -33.9186 151.1402 20.849104
## 47 ASN00066131 RIVERVIEW OBSERVATORY -33.8258 151.1556 21.009524
## 48 ASN00067104 BOX HILL (HYNDS ROAD) -33.6617 150.9000 21.120894
## 49 ASN00066189 WEST PYMBLE (WYUNA ROAD) -33.7693 151.1380 21.209116
## 50 ASN00067084 ORCHARD HILLS TREATMENT WORKS -33.8020 150.7069 21.288391
## 51 ASN00066204 OYSTER BAY (GREEN POINT ROAD) -34.0009 151.0738 21.391107
## 52 ASN00066158 TURRAMURRA (KISSING POINT ROAD -33.7366 151.1271 22.152621
## 53 ASN00066120 GORDON GOLF CLUB -33.7617 151.1462 22.258367
## 54 ASN00066078 LUCAS HEIGHTS (ANSTO) -34.0517 150.9800 22.897282
## 55 ASN00067015 BRINGELLY (MARYLAND) -33.9696 150.7250 23.124628
## 56 ASN00068160 CAMPBELLTOWN (KENTLYN (GEORGES -34.0542 150.8772 23.222412
## 57 ASN00068250 CAMDEN VALLEY GOLF RESORT -34.0128 150.7675 23.504569
## 58 ASN00066157 PYMBLE (CANISIUS COLLEGE) -33.7371 151.1521 24.058869
## 59 ASN00066058 SANS SOUCI (PUBLIC SCHOOL) -33.9942 151.1292 24.391064
## 60 ASN00067022 GALSTON (ROWLAND VILLAGE) -33.6550 151.0553 24.583503
## 61 ASN00068231 RUSE (DENISON STREET) -34.0630 150.8489 24.837610
## 62 ASN00066114 NORTH TURRAMURRA (DRYDEN RD) -33.7179 151.1470 24.858762
## 63 ASN00066037 SYDNEY AIRPORT AMO -33.9465 151.1731 24.870760
## 64 ASN00066062 SYDNEY (OBSERVATORY HILL) -33.8607 151.2050 25.421751
## 65 ASN00066011 CHATSWOOD BOWLING CLUB -33.8000 151.2000 25.553205
## 66 ASN00067115 GLENMORE PARK (CARTWRIGHT CL) -33.7826 150.6619 25.877100
## 67 ASN00066006 SYDNEY BOTANIC GARDENS -33.8662 151.2160 26.470162
## 68 ASN00066080 CASTLE COVE (ROSEBRIDGE AVE) -33.7809 151.2044 26.489163
## 69 ASN00066176 AUDLEY (ROYAL NATIONAL PARK) -34.0658 151.0567 26.689966
## 70 ASN00067029 WALLACIA POST OFFICE -33.8637 150.6410 26.729649
## 71 ASN00066206 ST IVES (RICHMOND AVENUE) -33.7096 151.1730 27.351861
## 72 ASN00067113 PENRITH LAKES AWS -33.7195 150.6783 27.416523
## 73 ASN00068257 CAMPBELLTOWN (MOUNT ANNAN) -34.0615 150.7735 27.594122
## 74 ASN00066073 RANDWICK RACECOURSE -33.9105 151.2276 28.284461
## 75 ASN00068254 MOUNT ANNAN BOTANIC GARDEN -34.0673 150.7678 28.418732
## 76 ASN00066160 CENTENNIAL PARK -33.8959 151.2341 28.535385
## 77 ASN00067031 WINDSOR BOWLING CLUB -33.6100 150.8151 28.724326
## 78 ASN00066188 BELROSE (EVELYN PLACE) -33.7402 151.2173 29.221247
## 79 ASN00066052 RANDWICK BOWLING CLUB -33.9096 151.2419 29.545881
## 80 ASN00066086 CRONULLA STP -34.0313 151.1642 29.549535
## 81 ASN00067010 GLENORIE (OLD NORTHERN RD) -33.5908 151.0094 29.742533
##
## $genesee1
## id name latitude longitude distance
## 1 USC00201150 MI BURTON 4N 43.0675 -83.5919 3.979309
## 2 US1MIGN0010 MI BURTON 0.9 NNW 43.0085 -83.6274 9.064849
## 3 US1MIGN0008 MI MOUNT MORRIS 3.1 WSW 43.1057 -83.7580 10.538333
## 4 US1MIGN0014 MI DAVISON 3.3 SW 43.0003 -83.5684 11.159857
## 5 US1MIGN0005 MI DAVISON 0.7 SSW 43.0219 -83.5246 11.431375
## 6 USC00202851 MI FLINT 7 W 43.0378 -83.7694 12.725462
## 7 USC00201645 MI CLIO 43.1794 -83.7369 13.193328
## 8 US1MIGN0009 MI BURTON 3.3 SW 42.9613 -83.6636 14.569099
## 9 USW00014826 MI FLINT BISHOP INTL AP 42.9667 -83.7494 16.797891
## 10 US1MIGN0023 MI GRAND BLANC 3.8 WNW 42.9440 -83.6886 16.919078
## 11 US1MISG0004 MI BIRCH RUN 2.6 ESE 43.2291 -83.7470 18.146491
## 12 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE 42.9897 -83.8166 18.824526
## 13 US1MIGN0015 MI FLINT 6.4 SSW 42.9326 -83.7210 19.001794
## 14 US1MIGN0018 MI GRAND BLANC 0.7 SE 42.9187 -83.6079 19.132279
## 15 USC00203278 MI GOODRICH 42.9164 -83.5097 21.640755
## 16 USC00204659 MI LAPEER 2W 43.0581 -83.3606 22.167570
## 17 US1MIGN0022 MI GRAND BLANC 2.9 SE 42.8909 -83.5858 22.428899
## 18 US1MIGN0004 MI MONTROSE 0.4 NW 43.1794 -83.8987 23.962697
## 19 USC00205488 MI MILLINGTON 3 SE 43.2836 -83.4792 24.756898
## 20 US1MILP0003 MI LAPEER 1.1 SSW 43.0316 -83.3293 25.277892
## 21 USC00204655 MI LAPEER WWTP 43.0608 -83.3075 26.394851
## 22 USC00202955 MI FRANKENMUTH 1SE 43.3194 -83.7161 26.445482
##
## $genesee2
## id name latitude longitude distance
## 1 USC00201150 MI BURTON 4N 43.0675 -83.5919 3.979309
## 2 US1MIGN0010 MI BURTON 0.9 NNW 43.0085 -83.6274 9.064849
## 3 US1MIGN0008 MI MOUNT MORRIS 3.1 WSW 43.1057 -83.7580 10.538333
## 4 US1MIGN0014 MI DAVISON 3.3 SW 43.0003 -83.5684 11.159857
## 5 US1MIGN0005 MI DAVISON 0.7 SSW 43.0219 -83.5246 11.431375
## 6 USC00202851 MI FLINT 7 W 43.0378 -83.7694 12.725462
## 7 US1MIGN0024 MI CLIO 0.4 SW 43.1725 -83.7423 12.930649
## 8 USC00201645 MI CLIO 43.1794 -83.7369 13.193328
## 9 US1MIGN0009 MI BURTON 3.3 SW 42.9613 -83.6636 14.569099
## 10 USW00014826 MI FLINT BISHOP INTL AP 42.9667 -83.7494 16.797891
## 11 US1MIGN0023 MI GRAND BLANC 3.8 WNW 42.9440 -83.6886 16.919078
## 12 US1MISG0004 MI BIRCH RUN 2.6 ESE 43.2291 -83.7470 18.146491
## 13 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE 42.9897 -83.8166 18.824526
## 14 US1MIGN0015 MI FLINT 6.4 SSW 42.9326 -83.7210 19.001794
## 15 US1MIGN0018 MI GRAND BLANC 0.7 SE 42.9187 -83.6079 19.132279
## 16 USC00203278 MI GOODRICH 42.9164 -83.5097 21.640755
## 17 USC00204659 MI LAPEER 2W 43.0581 -83.3606 22.167570
## 18 US1MIGN0022 MI GRAND BLANC 2.9 SE 42.8909 -83.5858 22.428899
## 19 US1MIGN0004 MI MONTROSE 0.4 NW 43.1794 -83.8987 23.962697
## 20 USC00205488 MI MILLINGTON 3 SE 43.2836 -83.4792 24.756898
## 21 US1MILP0003 MI LAPEER 1.1 SSW 43.0316 -83.3293 25.277892
## 22 USC00204655 MI LAPEER WWTP 43.0608 -83.3075 26.394851
## 23 USC00202955 MI FRANKENMUTH 1SE 43.3194 -83.7161 26.445482
##
## $columbus
## id name latitude longitude
## 1 US1OHFR0018 OH COLUMBUS 2.4 WNW 39.9977 -83.0323
## 2 US1OHFR0025 OH COLUMBUS 2.8 WSW 39.9804 -83.0397
## 3 US1OHFR0003 OH GRANDVIEW HEIGHTS 0.1 N 39.9810 -83.0401
## 4 USC00331785 OH COLUMBUS WCMH 40.0250 -83.0269
## 5 US1OHFR0034 OH COLUMBUS 3.6 NW 40.0191 -83.0437
## 6 US1OHFR0020 OH COLUMBUS 3.5 NE 40.0287 -82.9477
## 7 US1OHFR0001 OH UPPER ARLINGTON 0.9 E 40.0279 -83.0543
## 8 US1OHFR0021 OH MARBLE CLIFF 1.1 WNW 39.9931 -83.0786
## 9 US1OHFR0007 OH UPPER ARLINGTON 1.3 SSW 40.0112 -83.0832
## 10 USW00014821 OH COLUMBUS PORT COLUMBUS INTL AP 39.9914 -82.8808
## 11 USC00331783 OH COLUMBUS-VALLEY CROSSING 39.9047 -82.9200
## 12 US1OHFR0012 OH UPPER ARLINGTON 2.4 NNW 40.0604 -83.0815
## 13 USC00331777 OH COLUMBUS-HAP CREMEAN WP 40.0603 -82.8942
## 14 US1OHFR0024 OH COLUMBUS 9.3 NNE 40.0925 -82.9582
## 15 USW00004804 OH COLUMBUS OHIO STATE UNIV AP 40.0781 -83.0781
## 16 US1OHFR0037 OH REYNOLDSBURG 1.6 W 39.9588 -82.8294
## 17 US1OHFR0016 OH DUBLIN 3.7 ESE 40.0923 -83.0725
## 18 US1OHFR0022 OH GALLOWAY 3.1 N 39.9561 -83.1592
## 19 USC00331779 OH COLUMBUS-PARSONS AVE. 39.8469 -82.9872
## 20 USC00338951 OH WESTERVILLE 40.1264 -82.9433
## 21 US1OHFR0010 OH WESTERVILLE 0.2 WNW 40.1226 -82.9213
## 22 US1OHFR0030 OH HILLIARD 1.8 W 40.0344 -83.1768
## 23 US1OHFR0008 OH NEW ALBANY 2.8 SSE 40.0403 -82.7980
## 24 US1OHFR0002 OH DUBLIN 3.2 ENE 40.1299 -83.0742
## 25 US1OHLC0002 OH PATASKALA 4.4 WNW 40.0273 -82.7490
## 26 US1OHFF0005 OH PICKERINGTON 2.7 NNE 39.9263 -82.7469
## 27 US1OHDL0002 OH WESTERVILLE 4.0 N 40.1790 -82.9256
## 28 US1OHFR0023 OH HARRISBURG 3.7 WNW 39.8378 -83.2321
## 29 US1OHLC0011 OH PATASKALA 2.0 NE 40.0240 -82.6511
## distance
## 1 4.106137
## 2 4.234920
## 3 4.270197
## 4 5.909011
## 5 6.310790
## 6 6.504242
## 7 7.639623
## 8 7.687720
## 9 8.664212
## 10 9.389591
## 11 10.282003
## 12 11.858973
## 13 12.094496
## 14 12.799033
## 15 13.238469
## 16 13.887609
## 17 14.326951
## 18 14.662096
## 19 14.801971
## 20 16.757176
## 21 16.900247
## 22 17.021110
## 23 17.673463
## 24 18.143413
## 25 21.190424
## 26 21.564691
## 27 22.796070
## 28 26.008132
## 29 29.278406
##
## $bronx
## id name latitude longitude distance
## 1 USW00014732 NY NEW YORK LAGUARDIA AP 40.7794 -73.8803 5.616756
## 2 USW00094728 NY NEW YORK CNTRL PK TWR 40.7789 -73.9692 6.167420
## 3 USC00300961 NY BRONX 40.8369 -73.8494 6.230297
## 4 US1NJBG0018 NJ PALISADES PARK 0.2 WNW 40.8481 -74.0002 7.435652
## 5 US1NJBG0003 NJ TENAFLY 1.3 W 40.9147 -73.9775 11.587163
## 6 US1NYQN0002 NY MIDDLE VILLAGE 0.5 SW 40.7145 -73.8819 12.161951
## 7 USW00094741 NJ TETERBORO AP 40.8500 -74.0614 12.354793
## 8 US1NJBG0001 NJ BERGENFIELD 0.3 SW 40.9213 -74.0020 13.206762
## 9 US1NJBG0012 NJ WOOD RIDGE 0.6 SE 40.8420 -74.0830 13.930427
## 10 US1NJBG0033 NJ WOOD RIDGE 0.6 NNW 40.8536 -74.0943 15.131881
## 11 US1NYWC0009 NY NEW ROCHELLE 1.3 S 40.9040 -73.7770 15.226895
## 12 US1NJBG0013 NJ RUTHERFORD 1.2 N 40.8373 -74.1065 15.809146
## 13 US1NYKN0025 NY BROOKLYN 3.1 NW 40.6846 -73.9867 16.069963
## 14 US1NJBG0031 NJ DEMAREST 0.6 NNW 40.9628 -73.9600 16.230719
## 15 US1NJBG0002 NJ SADDLE BROOK TWP 0.6 E 40.9027 -74.0834 16.534408
## 16 US1NJBG0011 NJ NORTH ARLINGTON 0.7 NE 40.7944 -74.1190 16.988985
## 17 US1NJBG0008 NJ SADDLE BROOK TWP 0.3 NNE 40.9071 -74.0934 17.505118
## 18 USC00286146 NJ NEW MILFORD 40.9611 -74.0158 17.635536
## 19 US1NJBG0015 NJ NORTH ARLINGTON 0.7 WNW 40.7915 -74.1398 18.769309
## 20 US1NJHD0002 NJ KEARNY 1.7 NW 40.7729 -74.1409 19.318492
## 21 US1NJBG0005 NJ WESTWOOD 0.8 ESE 40.9830 -74.0159 19.836072
## 22 US1NJBG0010 NJ RIVER VALE TWP 1.5 S 40.9915 -74.0123 20.587159
## 23 US1NYNS0007 NY FLORAL PARK 0.4 W 40.7230 -73.7110 20.642010
## 24 US1NJHD0001 NJ HARRISON 0.3 N 40.7480 -74.1518 21.094543
## 25 USC00283704 NJ HARRISON 40.7514 -74.1567 21.338273
## 26 US1NJBG0020 NJ PARAMUS 1.8 NNW 40.9682 -74.0902 21.822558
## 27 USC00302129 NY DOBBS FERRY-ARDSLEY 41.0072 -73.8344 22.023427
## 28 US1NJBG0017 NJ GLEN ROCK 0.7 SSE 40.9511 -74.1183 22.145027
## 29 US1NJES0020 NJ BLOOMFIELD 1.7 S 40.7850 -74.1885 22.932509
## 30 US1NYKN0003 NY BROOKLYN 2.4 SW 40.6194 -73.9859 22.986706
## 31 US1NYWC0005 NY HARRISON 4.1 SSW 40.9639 -73.7232 23.014851
## 32 USC00307587 NY SEA CLIFF 40.8506 -73.6483 23.109762
## 33 US1NJBG0037 NJ GLEN ROCK 0.4 WNW 40.9614 -74.1328 23.815579
## 34 US1NJPS0014 NJ HAWTHORNE 1.0 SSE 40.9436 -74.1523 23.880745
## 35 USC00289832 NJ WOODCLIFF LAKE 41.0139 -74.0425 23.891667
## 36 US1NJES0015 NJ MONTCLAIR 2.2 NNE 40.8565 -74.2004 23.935385
## 37 USW00094789 NY NEW YORK JFK INTL AP 40.6386 -73.7622 24.159135
## 38 US1NJPS0017 NJ WOODLAND PARK 0.1 NW 40.8918 -74.1960 24.547061
## 39 US1NJPS0005 NJ HAWTHORNE 0.4 S 40.9519 -74.1577 24.787058
## 40 US1NJES0011 NJ CEDAR GROVE TWP 0.9 NE 40.8648 -74.2157 25.368252
## 41 USC00305796 NY NY AVE V BROOKLYN 40.5939 -73.9808 25.658206
## 42 US1NJPS0018 NJ PATERSON 2.0 W 40.9163 -74.2005 25.903423
## 43 USW00014734 NJ NEWARK INTL AP 40.6825 -74.1694 25.982975
## 44 US1NJPS0003 NJ LITTLE FALLS TWP 0.2 NNW 40.8788 -74.2205 26.107408
## 45 US1NJPS0012 NJ LITTLE FALLS TWP 0.5 WNW 40.8796 -74.2270 26.658883
## 46 USC00284887 NJ LITTLE FALLS 40.8858 -74.2261 26.764590
## 47 US1NJES0024 NJ CEDAR GROVE TWP 0.4 W 40.8557 -74.2356 26.845270
## 48 US1NYNS0014 NY LYNBROOK 0.3 NW 40.6623 -73.6780 26.891776
## 49 USC00285503 NJ MIDLAND PARK 40.9939 -74.1453 27.062890
## 50 USC00305377 NY MINEOLA 40.7328 -73.6183 27.191818
## 51 US1NJPS0004 NJ NORTH HALEDON 0.6 N 40.9713 -74.1856 27.953822
## 52 US1NJES0010 NJ VERONA TWP 0.7 SW 40.8255 -74.2531 28.035405
## 53 US1NJES0021 NJ VERONA TWP 0.6 WSW 40.8305 -74.2539 28.119240
## 54 US1NJES0004 NJ NORTH CALDWELL 0.6 SSE 40.8576 -74.2523 28.265572
## 55 US1NJPS0008 NJ WAYNE TWP 1.1 ESE 40.9412 -74.2267 29.094312
## 56 US1NYWC0003 NY WHITE PLAINS 3.1 NNW 41.0639 -73.7722 29.826697
## 57 US1NYNS0009 NY MILL NECK 1.1 SW 40.8704 -73.5717 29.828998
## 58 US1NYRL0005 NY WEST NYACK 1.3 WSW 41.0835 -73.9930 29.934369Not all the locations have stations nearby. Therefore, I will omit them from the weather data evaluation using the following code.
has_stations <- sapply(stations, function(x) nrow(x) > 0)
outbreak_loc_true <- outbreak_loc %>% filter(has_stations)
outbreak_loc_true## id file_id latitude longitude year_min date_min
## 1 pittsburgh pittsburgh 40.43 -79.98 2002 2002-01-01
## 2 quebec quebec 46.85 -71.34 2002 2002-01-01
## 3 miyazaki miyazaki 31.89 131.34 1992 1992-01-01
## 4 pamplona pamplona 42.81 -1.65 1996 1996-01-01
## 5 rapid city rapid_city 44.06 -103.22 1995 1995-01-01
## 6 sarpsborg sarpsborg 59.28 11.08 1995 1995-01-01
## 7 murcia murcia 37.98 -1.12 1991 1991-01-01
## 8 melbourne melbourne -37.86 145.07 1990 1990-01-01
## 9 bovenkarspel bovenkarspel 52.70 5.24 1989 1989-01-01
## 10 london london 51.52 -0.10 1979 1979-01-01
## 11 sydney sydney -33.85 150.93 2006 2006-01-01
## 12 genesee1 genesee1 43.09 -83.63 2004 2004-01-01
## 13 genesee2 genesee2 43.09 -83.63 2005 2005-01-01
## 14 columbus columbus 39.98 -82.99 2003 2003-01-01
## 15 bronx bronx 40.82 -73.92 2005 2005-01-01
## year_max date_max onset before_onset
## 1 2012 2012-12-31 2012-08-26 2012-08-12
## 2 2012 2012-12-31 2012-07-18 2012-07-04
## 3 2002 2002-12-31 2002-07-18 2002-07-04
## 4 2006 2006-12-31 2006-06-01 2006-05-18
## 5 2005 2005-12-31 2005-05-26 2005-05-12
## 6 2005 2005-12-31 2005-05-12 2005-04-28
## 7 2001 2001-12-31 2001-06-26 2001-06-12
## 8 2000 2000-12-31 2000-04-17 2000-04-03
## 9 1999 1999-12-31 1999-02-25 1999-02-11
## 10 1989 1989-12-31 1989-01-01 1988-12-18
## 11 2016 2016-12-31 2016-04-25 2016-04-11
## 12 2014 2014-12-31 2014-06-06 2014-05-23
## 13 2015 2015-12-31 2015-05-04 2015-04-20
## 14 2013 2013-12-31 2013-07-09 2013-06-25
## 15 2015 2015-12-31 2015-07-12 2015-06-28Using the countyweather codes I can gather the data for each station in a loop. The code gathers the weather data for each stations and averages them. Then I saved all the data as rds. files because they take a long time to gather. The data is saved in a folder I created called “weather_files/”
for(i in which(has_stations))
{
meteo_df <- meteo_pull_monitors(monitors = stations[[i]]$id,
keep_flags = FALSE,
date_min = outbreak_loc$date_min[i],
date_max = outbreak_loc$date_max[i],
var = c("prcp","snow","snwd","tmax","tmin","tavg"))
coverage_df <- rnoaa::meteo_coverage(meteo_df, verbose = FALSE)
filtered <- countyweather:::filter_coverage(coverage_df, 0.90)
good_monitors <- unique(filtered$id)
filtered_data <- dplyr::filter(meteo_df, id %in% good_monitors)
averaged <- countyweather:::ave_daily(filtered_data)
# For metrics that are reported in tenths of units (precipitation
# and temperature), divide by 10 to get values in degrees Celsius and
# millimeters
which_tenth_units <- which(colnames(averaged) %in%
c("prcp", "tavg", "tmax", "tmin"))
averaged[ , which_tenth_units] <- averaged[ , which_tenth_units] / 10
file_name <- paste0("weather_files/", outbreak_loc$file_id[i], ".rds")
saveRDS(averaged, file_name)
}Now that all of the data is gathered and averaged I can plot the data. The loop will go through the files in order which is in alphabetical order. Therefore I must order my outbreak data frame into alphabetical order too. I will rename this data frame as df_stations for plotting.
## id file_id latitude longitude year_min date_min
## 1 bovenkarspel bovenkarspel 52.70 5.24 1989 1989-01-01
## 2 bronx bronx 40.82 -73.92 2005 2005-01-01
## 3 columbus columbus 39.98 -82.99 2003 2003-01-01
## 4 genesee1 genesee1 43.09 -83.63 2004 2004-01-01
## 5 genesee2 genesee2 43.09 -83.63 2005 2005-01-01
## 6 london london 51.52 -0.10 1979 1979-01-01
## 7 melbourne melbourne -37.86 145.07 1990 1990-01-01
## 8 miyazaki miyazaki 31.89 131.34 1992 1992-01-01
## 9 murcia murcia 37.98 -1.12 1991 1991-01-01
## 10 pamplona pamplona 42.81 -1.65 1996 1996-01-01
## 11 pittsburgh pittsburgh 40.43 -79.98 2002 2002-01-01
## 12 quebec quebec 46.85 -71.34 2002 2002-01-01
## 13 rapid city rapid_city 44.06 -103.22 1995 1995-01-01
## 14 sarpsborg sarpsborg 59.28 11.08 1995 1995-01-01
## 15 sydney sydney -33.85 150.93 2006 2006-01-01
## year_max date_max onset before_onset
## 1 1999 1999-12-31 1999-02-25 1999-02-11
## 2 2015 2015-12-31 2015-07-12 2015-06-28
## 3 2013 2013-12-31 2013-07-09 2013-06-25
## 4 2014 2014-12-31 2014-06-06 2014-05-23
## 5 2015 2015-12-31 2015-05-04 2015-04-20
## 6 1989 1989-12-31 1989-01-01 1988-12-18
## 7 2000 2000-12-31 2000-04-17 2000-04-03
## 8 2002 2002-12-31 2002-07-18 2002-07-04
## 9 2001 2001-12-31 2001-06-26 2001-06-12
## 10 2006 2006-12-31 2006-06-01 2006-05-18
## 11 2012 2012-12-31 2012-08-26 2012-08-12
## 12 2012 2012-12-31 2012-07-18 2012-07-04
## 13 2005 2005-12-31 2005-05-26 2005-05-12
## 14 2005 2005-12-31 2005-05-12 2005-04-28
## 15 2016 2016-12-31 2016-04-25 2016-04-11PLOT 1
Outbreak Distribution
This plot is divided by outbreaks in the northern and southern hemisphere. This allows us to see when the outbreaks generally occur in the year.
PLOT 2
10 years for all data
These plots allow for a quick glance into all the weather variables for each location.
## Warning: Removed 5 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_path).
PLOT 3
TMAX and TMIN
## Warning: Removed 1100 rows containing missing values (geom_path).
## Warning: Removed 5 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 55 rows containing missing values (geom_path).
PLOT 4
Precipitation
I also made a loop to plot graphs and histograms of the data with lines indicating each day before the start of the outbreak for a total of 14 days.A plot of the percentiles is also included. The precentile data is saved for a plot of percentiles as shown later.
PLOT 5
TMAX
PLOT 6
TMIN
## Warning: Removed 550 rows containing missing values (geom_path).
## Warning: Removed 550 rows containing non-finite values (stat_bin).
## Warning: Removed 5 rows containing missing values (geom_path).
## Warning: Removed 1587 rows containing non-finite values (stat_bin).## Warning: Removed 9 rows containing missing values (geom_vline).
## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1595 rows containing non-finite values (stat_bin).## Warning: Removed 5 rows containing missing values (geom_vline).
## Warning: Removed 5 rows containing missing values (position_stack).
## Warning: Removed 975 rows containing non-finite values (stat_bin).
## Warning: Removed 27 rows containing missing values (geom_path).
## Warning: Removed 27 rows containing non-finite values (stat_bin).
PERCENTILES
Table and Plots
Now I gathered a 2-week seasonal subset data for each weather variable and plotted it in a single table. I saved the table data for each outbreak and plotted them in a facetted plot.s
## days_before_onset TMAX_year TMAX_seasonal TMIN_year TMIN_seasonal
## 1 0 15.690799 54.814815 20.478800 56.296296
## 2 1 13.700606 48.148148 15.806172 47.407407
## 3 2 14.681281 52.592593 18.257860 51.851852
## 4 3 11.479665 41.481481 13.614076 42.222222
## 5 4 20.334583 64.444444 25.007211 66.666667
## 6 5 15.690799 54.814815 23.622729 63.703704
## 7 6 26.593597 74.074074 24.257283 65.925926
## 8 7 18.402077 58.518519 18.257860 51.851852
## 9 8 9.662532 35.555556 9.085665 33.333333
## 10 9 16.094606 57.037037 11.681569 37.037037
## 11 10 12.027690 43.703704 13.614076 42.222222
## 12 11 6.864725 28.888889 3.345832 14.814815
## 13 12 3.663109 13.333333 2.278627 10.370370
## 14 13 6.201327 25.925926 5.018748 20.000000
## 15 14 7.095472 29.629630 5.797519 22.222222
## 16 0 93.104307 67.272727 90.589993 55.757576
## 17 1 90.540204 61.818182 87.229276 44.848485
## 18 2 84.266866 37.575758 92.805576 67.272727
## 19 3 77.346278 19.393939 88.150361 47.878788
## 20 4 93.552402 69.090909 99.004232 94.545455
## 21 5 92.108539 64.848485 97.684839 89.090909
## 22 6 83.619617 34.545455 90.589993 55.757576
## 23 7 80.383371 29.090909 85.262634 36.969697
## 24 8 72.417227 10.909091 86.706497 41.212121
## 25 9 80.084640 27.878788 78.790142 20.000000
## 26 10 77.918845 21.212121 85.362211 37.575758
## 27 11 84.092606 36.363636 88.399303 49.090909
## 28 12 78.317152 23.636364 80.657207 24.242424
## 29 13 70.176749 6.060606 74.583022 12.727273
## 30 14 65.098332 2.424242 72.840428 6.060606
## 31 0 93.504231 72.727273 98.307616 96.969697
## 32 1 77.277252 27.878788 87.605774 52.727273
## 33 2 78.720757 32.121212 89.447486 60.000000
## 34 3 82.901941 40.606061 97.137880 93.333333
## 35 4 70.408163 13.939394 91.762071 69.696970
## 36 5 71.577899 18.181818 97.735192 95.757576
## 37 6 84.693878 46.666667 94.673967 84.848485
## 38 7 79.193629 34.545455 92.956695 75.151515
## 39 8 76.655052 26.060606 88.899950 56.969697
## 40 9 72.025884 18.787879 78.795421 29.696970
## 41 10 74.813340 21.818182 79.417621 31.515152
## 42 11 83.026381 41.212121 88.402190 54.545455
## 43 12 87.730214 54.545455 91.762071 69.696970
## 44 13 91.513191 65.454545 91.762071 69.696970
## 45 14 97.859632 89.696970 96.689895 90.909091
## 46 0 72.573420 52.727273 61.796914 23.030303
## 47 1 61.896466 21.212121 65.505226 36.363636
## 48 2 73.693380 56.969697 78.546541 60.606061
## 49 3 89.646590 80.000000 96.042807 98.181818
## 50 4 92.160279 86.060606 88.128422 81.212121
## 51 5 86.286710 75.151515 74.763564 55.757576
## 52 6 86.112494 73.939394 75.012444 56.969697
## 53 7 72.000996 49.696970 67.073171 41.212121
## 54 8 65.878547 36.969697 63.787954 29.090909
## 55 9 84.146341 71.515152 82.030861 70.909091
## 56 10 90.019910 80.606061 86.137382 80.000000
## 57 11 86.485814 75.757576 74.539572 54.545455
## 58 12 76.704828 60.606061 65.281234 35.151515
## 59 13 62.792434 24.242424 61.697362 21.818182
## 60 14 64.211050 29.696970 62.667994 24.242424
## 61 0 80.358476 95.757576 77.346278 96.969697
## 62 1 72.641275 90.909091 61.463779 76.969697
## 63 2 67.064974 80.000000 54.742345 60.606061
## 64 3 52.128454 47.878788 49.639034 46.060606
## 65 4 57.181977 64.848485 52.725915 54.545455
## 66 5 54.543191 56.969697 43.241225 30.303030
## 67 6 42.618870 26.060606 34.229525 12.121212
## 68 7 44.983819 32.121212 41.224795 25.454545
## 69 8 42.768235 27.272727 31.715210 7.272727
## 70 9 42.544187 24.848485 31.167538 6.060606
## 71 10 28.055763 3.030303 21.483694 1.212121
## 72 11 24.794623 1.212121 31.814787 8.484848
## 73 12 32.636296 6.666667 46.502365 40.606061
## 74 13 51.058003 46.666667 52.003983 52.727273
## 75 14 58.675629 69.090909 57.729649 68.484848
## 76 0 NaN NaN NaN NaN
## 77 1 19.630872 18.765091 NaN NaN
## 78 2 NaN NaN NaN NaN
## 79 3 NaN NaN 43.150967 42.361405
## 80 4 NaN NaN NaN NaN
## 81 5 NaN NaN NaN NaN
## 82 6 NaN NaN 65.981078 65.213711
## 83 7 34.697987 33.425319 43.150967 42.361405
## 84 8 42.248322 40.772680 NaN NaN
## 85 9 42.248322 40.772680 NaN NaN
## 86 10 34.697987 33.425319 64.253394 63.478629
## 87 11 34.697987 33.425319 44.055944 43.250106
## 88 12 NaN NaN NaN NaN
## 89 13 35.436242 34.046223 NaN NaN
## 90 14 NaN NaN 22.459893 21.836648
## 91 0 54.131409 46.060606 53.111000 44.848485
## 92 1 72.722748 67.878788 84.544550 87.272727
## 93 2 71.304131 66.666667 56.943753 48.484848
## 94 3 68.292683 65.454545 94.524639 95.757576
## 95 4 79.019413 78.787879 90.219014 92.727273
## 96 5 85.689398 89.696970 72.772524 72.121212
## 97 6 85.988054 90.303030 79.790941 81.818182
## 98 7 82.055749 81.818182 74.489796 74.545455
## 99 8 87.431558 92.121212 66.177203 63.030303
## 100 9 82.503733 82.424242 31.408661 13.939394
## 101 10 61.448482 58.181818 41.961175 28.484848
## 102 11 58.536585 52.727273 36.635142 22.424242
## 103 12 53.583873 44.848485 76.754604 77.575758
## 104 13 72.175212 67.272727 52.040816 43.636364
## 105 14 74.066700 70.303030 70.930811 69.696970
## 106 0 76.093071 16.149068 NaN NaN
## 107 1 86.934288 48.447205 82.872472 27.586207
## 108 2 92.815137 64.596273 NaN NaN
## 109 3 83.175658 34.161491 NaN NaN
## 110 4 88.391716 52.173913 97.111019 87.068966
## 111 5 90.948606 57.763975 98.060256 93.965517
## 112 6 87.241115 49.068323 85.266199 42.241379
## 113 7 87.496804 49.689441 83.945522 35.344828
## 114 8 97.519816 81.366460 NaN NaN
## 115 9 87.599080 50.310559 97.853900 91.379310
## 116 10 88.263871 51.552795 87.825010 51.724138
## 117 11 84.709793 41.614907 84.275691 38.793103
## 118 12 83.584761 36.645963 91.209245 64.655172
## 119 13 77.371516 19.875776 NaN NaN
## 120 14 77.090258 18.633540 95.542716 81.896552
## 121 0 99.850672 100.000000 91.388751 95.757576
## 122 1 99.004480 98.181818 84.395222 84.242424
## 123 2 98.282728 95.757576 84.494774 85.454545
## 124 3 99.825784 99.393939 78.496765 68.484848
## 125 4 99.153808 98.787879 73.544052 50.909091
## 126 5 93.852663 90.909091 68.367347 30.303030
## 127 6 79.442509 60.606061 63.862618 13.939394
## 128 7 63.364858 16.969697 72.847188 47.878788
## 129 8 62.792434 15.151515 84.046789 83.030303
## 130 9 75.684420 49.090909 67.098059 26.060606
## 131 10 86.361374 79.393939 90.467894 95.151515
## 132 11 96.416127 92.727273 78.496765 68.484848
## 133 12 94.848183 92.121212 79.890493 73.333333
## 134 13 87.680438 81.818182 69.362867 33.939394
## 135 14 63.937282 17.575758 65.355898 18.787879
## 136 0 60.129418 30.303030 39.347934 8.484848
## 137 1 46.565455 13.333333 51.294176 25.454545
## 138 2 45.644599 11.515152 62.020906 51.515152
## 139 3 63.937282 36.363636 78.571429 85.454545
## 140 4 88.626182 86.666667 80.537581 87.272727
## 141 5 92.458935 93.939394 69.437531 67.272727
## 142 6 75.385764 58.787879 74.912892 79.393939
## 143 7 72.598308 51.515152 37.406670 6.666667
## 144 8 56.470881 24.848485 28.297661 1.212121
## 145 9 42.508711 6.060606 42.483823 11.515152
## 146 10 63.265306 35.151515 66.923843 62.424242
## 147 11 85.813838 81.212121 72.125436 75.151515
## 148 12 65.778995 40.000000 62.344450 52.727273
## 149 13 63.066202 34.545455 54.952713 30.303030
## 150 14 65.778995 40.000000 82.205077 89.696970
## 151 0 92.906919 83.636364 85.365854 53.333333
## 152 1 92.906919 83.636364 82.478845 44.848485
## 153 2 91.886511 78.181818 78.596317 32.121212
## 154 3 84.420110 50.909091 74.589348 18.787879
## 155 4 78.919861 30.909091 71.353907 11.515152
## 156 5 75.958188 24.242424 72.448980 15.151515
## 157 6 78.944749 31.515152 76.679940 24.848485
## 158 7 75.236436 23.636364 76.779492 26.060606
## 159 8 72.672972 16.969697 78.496765 31.515152
## 160 9 79.417621 33.333333 89.472374 67.272727
## 161 10 87.605774 61.818182 83.349925 47.272727
## 162 11 80.711797 36.969697 87.580886 63.030303
## 163 12 85.788950 55.757576 87.282230 61.818182
## 164 13 79.766053 33.939394 83.822797 49.090909
## 165 14 67.894475 9.696970 83.051269 46.060606
## 166 0 83.897461 32.121212 87.755102 38.787879
## 167 1 73.842708 10.909091 97.336984 84.242424
## 168 2 91.314087 60.606061 99.128920 93.333333
## 169 3 99.253360 96.363636 93.529119 66.666667
## 170 4 97.187656 84.848485 97.212544 83.636364
## 171 5 99.104032 95.151515 95.843703 77.575758
## 172 6 97.038328 83.636364 86.436038 34.545455
## 173 7 91.015431 58.787879 73.544052 2.424242
## 174 8 86.460926 41.212121 80.338477 16.969697
## 175 9 85.042310 38.181818 82.852165 23.636364
## 176 10 79.168741 19.393939 89.024390 43.636364
## 177 11 96.042807 80.000000 98.158288 87.878788
## 178 12 93.056247 67.878788 93.728223 67.878788
## 179 13 90.841215 56.969697 94.300647 70.909091
## 180 14 81.981085 27.878788 93.529119 66.666667
## 181 0 52.065704 30.909091 61.523146 43.030303
## 182 1 52.090592 31.515152 63.339970 49.090909
## 183 2 64.410154 61.212121 71.378795 77.575758
## 184 3 73.668492 86.060606 76.356396 90.909091
## 185 4 76.331508 92.121212 70.109507 75.757576
## 186 5 79.965157 95.151515 81.159781 96.969697
## 187 6 86.162270 98.181818 82.105525 97.575758
## 188 7 84.619214 97.575758 73.444500 86.060606
## 189 8 70.681931 79.393939 73.295172 85.454545
## 190 9 70.532603 77.575758 72.672972 84.242424
## 191 10 75.136884 88.484848 72.025884 80.606061
## 192 11 66.898955 68.484848 45.943255 8.484848
## 193 12 45.196615 20.000000 44.997511 7.272727
## 194 13 39.696366 9.696970 42.259831 5.454545
## 195 14 26.903932 1.818182 38.352414 3.030303
## 196 0 73.184358 72.500000 40.354913 14.166667
## 197 1 64.344397 65.833333 46.171541 25.833333
## 198 2 50.607953 31.666667 46.565889 27.500000
## 199 3 50.607953 31.666667 44.134078 21.666667
## 200 4 49.030562 26.666667 44.134078 21.666667
## 201 5 52.152481 37.500000 48.209004 35.833333
## 202 6 58.724942 52.500000 46.335853 26.666667
## 203 7 52.152481 37.500000 34.538285 6.666667
## 204 8 52.875452 38.333333 65.856063 87.500000
## 205 9 58.724942 52.500000 63.851462 83.333333
## 206 10 50.607953 31.666667 59.710812 75.000000
## 207 11 62.668419 60.000000 59.710812 75.000000
## 208 12 47.321722 20.833333 60.893855 76.666667
## 209 13 57.837660 50.000000 52.875452 50.833333
## 210 14 64.344397 65.833333 42.885311 18.333333
## 211 0 52.791109 53.333333 41.414141 21.818182
## 212 1 46.956302 40.000000 59.772727 72.727273
## 213 2 32.306138 17.575758 63.358586 81.212121
## 214 3 69.133620 85.454545 59.545455 71.515152
## 215 4 77.721647 92.121212 62.095960 77.575758
## 216 5 70.093458 86.666667 59.848485 73.333333
## 217 6 58.651175 65.454545 65.984848 85.454545
## 218 7 54.079313 55.151515 56.363636 61.818182
## 219 8 50.543066 48.484848 80.429293 96.969697
## 220 9 81.156858 94.545455 55.075758 55.757576
## 221 10 76.483961 90.303030 58.813131 69.696970
## 222 11 53.447840 53.939394 54.898990 55.151515
## 223 12 57.438747 62.424242 56.565657 62.424242
## 224 13 49.078050 44.848485 74.015152 94.545455
## 225 14 70.346047 87.272727 43.787879 27.272727
## PRCP_year PRCP_seasonal outbreak
## 1 53.33333 67.87879 1
## 2 40.00000 39.39394 1
## 3 17.57576 60.60606 1
## 4 85.45455 92.12121 1
## 5 92.12121 90.90909 1
## 6 86.66667 43.03030 1
## 7 65.45455 56.96970 1
## 8 55.15152 80.60606 1
## 9 48.48485 52.72727 1
## 10 94.54545 69.69697 1
## 11 90.30303 83.63636 1
## 12 53.93939 64.84848 1
## 13 62.42424 18.78788 1
## 14 44.84848 15.15152 1
## 15 87.27273 21.81818 1
## 16 53.33333 30.90909 2
## 17 40.00000 30.90909 2
## 18 17.57576 86.66667 2
## 19 85.45455 90.90909 2
## 20 92.12121 63.03030 2
## 21 86.66667 44.84848 2
## 22 65.45455 30.90909 2
## 23 55.15152 58.78788 2
## 24 48.48485 35.15152 2
## 25 94.54545 38.78788 2
## 26 90.30303 39.39394 2
## 27 53.93939 93.33333 2
## 28 62.42424 38.78788 2
## 29 44.84848 54.54545 2
## 30 87.27273 97.57576 2
## 31 53.33333 90.30303 3
## 32 40.00000 95.75758 3
## 33 17.57576 77.57576 3
## 34 85.45455 73.93939 3
## 35 92.12121 94.54545 3
## 36 86.66667 63.03030 3
## 37 65.45455 37.57576 3
## 38 55.15152 49.09091 3
## 39 48.48485 79.39394 3
## 40 94.54545 93.33333 3
## 41 90.30303 87.87879 3
## 42 53.93939 62.42424 3
## 43 62.42424 84.84848 3
## 44 44.84848 92.12121 3
## 45 87.27273 60.00000 3
## 46 53.33333 36.96970 4
## 47 40.00000 51.51515 4
## 48 17.57576 50.90909 4
## 49 85.45455 69.69697 4
## 50 92.12121 57.57576 4
## 51 86.66667 36.96970 4
## 52 65.45455 36.96970 4
## 53 55.15152 36.96970 4
## 54 48.48485 76.96970 4
## 55 94.54545 87.27273 4
## 56 90.30303 83.03030 4
## 57 53.93939 36.96970 4
## 58 62.42424 36.96970 4
## 59 44.84848 36.96970 4
## 60 87.27273 36.96970 4
## 61 53.33333 63.63636 5
## 62 40.00000 34.54545 5
## 63 17.57576 34.54545 5
## 64 85.45455 48.48485 5
## 65 92.12121 67.27273 5
## 66 86.66667 52.12121 5
## 67 65.45455 34.54545 5
## 68 55.15152 42.42424 5
## 69 48.48485 34.54545 5
## 70 94.54545 34.54545 5
## 71 90.30303 34.54545 5
## 72 53.93939 53.93939 5
## 73 62.42424 49.69697 5
## 74 44.84848 69.69697 5
## 75 87.27273 84.84848 5
## 76 53.33333 54.46880 6
## 77 40.00000 54.46880 6
## 78 17.57576 NaN 6
## 79 85.45455 54.46880 6
## 80 92.12121 54.46880 6
## 81 86.66667 NaN 6
## 82 65.45455 54.46880 6
## 83 55.15152 NaN 6
## 84 48.48485 NaN 6
## 85 94.54545 54.46880 6
## 86 90.30303 54.46880 6
## 87 53.93939 54.46880 6
## 88 62.42424 54.46880 6
## 89 44.84848 64.85666 6
## 90 87.27273 54.46880 6
## 91 53.33333 80.60606 7
## 92 40.00000 63.03030 7
## 93 17.57576 86.66667 7
## 94 85.45455 83.03030 7
## 95 92.12121 18.18182 7
## 96 86.66667 18.18182 7
## 97 65.45455 37.57576 7
## 98 55.15152 18.18182 7
## 99 48.48485 18.18182 7
## 100 94.54545 24.24242 7
## 101 90.30303 18.18182 7
## 102 53.93939 59.39394 7
## 103 62.42424 98.18182 7
## 104 44.84848 18.78788 7
## 105 87.27273 44.24242 7
## 106 53.33333 78.04878 8
## 107 40.00000 40.24390 8
## 108 17.57576 29.26829 8
## 109 85.45455 69.51220 8
## 110 92.12121 34.14634 8
## 111 86.66667 30.48780 8
## 112 65.45455 12.19512 8
## 113 55.15152 31.70732 8
## 114 48.48485 12.19512 8
## 115 94.54545 45.12195 8
## 116 90.30303 29.26829 8
## 117 53.93939 NaN 8
## 118 62.42424 86.58537 8
## 119 44.84848 81.70732 8
## 120 87.27273 32.92683 8
## 121 53.33333 89.69697 9
## 122 40.00000 89.69697 9
## 123 17.57576 89.69697 9
## 124 85.45455 89.69697 9
## 125 92.12121 89.69697 9
## 126 86.66667 89.69697 9
## 127 65.45455 89.69697 9
## 128 55.15152 89.69697 9
## 129 48.48485 89.69697 9
## 130 94.54545 89.69697 9
## 131 90.30303 89.69697 9
## 132 53.93939 89.69697 9
## 133 62.42424 89.69697 9
## 134 44.84848 89.69697 9
## 135 87.27273 89.69697 9
## 136 53.33333 70.30303 10
## 137 40.00000 70.30303 10
## 138 17.57576 70.30303 10
## 139 85.45455 70.30303 10
## 140 92.12121 70.30303 10
## 141 86.66667 70.30303 10
## 142 65.45455 70.30303 10
## 143 55.15152 70.30303 10
## 144 48.48485 70.30303 10
## 145 94.54545 70.30303 10
## 146 90.30303 77.57576 10
## 147 53.93939 70.30303 10
## 148 62.42424 70.30303 10
## 149 44.84848 70.30303 10
## 150 87.27273 70.30303 10
## 151 53.33333 35.75758 11
## 152 40.00000 35.75758 11
## 153 17.57576 35.75758 11
## 154 85.45455 53.33333 11
## 155 92.12121 62.42424 11
## 156 86.66667 52.72727 11
## 157 65.45455 63.03030 11
## 158 55.15152 43.63636 11
## 159 48.48485 69.09091 11
## 160 94.54545 71.51515 11
## 161 90.30303 35.75758 11
## 162 53.93939 80.00000 11
## 163 62.42424 66.06061 11
## 164 44.84848 40.60606 11
## 165 87.27273 44.84848 11
## 166 53.33333 30.30303 12
## 167 40.00000 89.69697 12
## 168 17.57576 68.48485 12
## 169 85.45455 99.39394 12
## 170 92.12121 30.30303 12
## 171 86.66667 30.30303 12
## 172 65.45455 30.30303 12
## 173 55.15152 30.30303 12
## 174 48.48485 30.30303 12
## 175 94.54545 30.30303 12
## 176 90.30303 30.30303 12
## 177 53.93939 30.30303 12
## 178 62.42424 30.30303 12
## 179 44.84848 30.90909 12
## 180 87.27273 72.72727 12
## 181 53.33333 56.96970 13
## 182 40.00000 95.15152 13
## 183 17.57576 83.63636 13
## 184 85.45455 33.33333 13
## 185 92.12121 33.33333 13
## 186 86.66667 33.33333 13
## 187 65.45455 33.33333 13
## 188 55.15152 33.33333 13
## 189 48.48485 37.57576 13
## 190 94.54545 55.75758 13
## 191 90.30303 33.33333 13
## 192 53.93939 33.33333 13
## 193 62.42424 33.33333 13
## 194 44.84848 56.96970 13
## 195 87.27273 97.57576 13
## 196 53.33333 41.21212 14
## 197 40.00000 45.45455 14
## 198 17.57576 66.66667 14
## 199 85.45455 73.93939 14
## 200 92.12121 75.75758 14
## 201 86.66667 36.36364 14
## 202 65.45455 36.36364 14
## 203 55.15152 50.30303 14
## 204 48.48485 36.36364 14
## 205 94.54545 64.24242 14
## 206 90.30303 78.78788 14
## 207 53.93939 80.60606 14
## 208 62.42424 60.00000 14
## 209 44.84848 89.69697 14
## 210 87.27273 36.36364 14
## 211 53.33333 38.18182 15
## 212 40.00000 81.21212 15
## 213 17.57576 73.93939 15
## 214 85.45455 46.06061 15
## 215 92.12121 18.78788 15
## 216 86.66667 62.42424 15
## 217 65.45455 83.03030 15
## 218 55.15152 71.51515 15
## 219 48.48485 53.33333 15
## 220 94.54545 40.60606 15
## 221 90.30303 52.72727 15
## 222 53.93939 54.54545 15
## 223 62.42424 53.93939 15
## 224 44.84848 70.30303 15
## 225 87.27273 14.54545 15## Warning: Removed 8 rows containing missing values (position_stack).
## Warning: Removed 8 rows containing missing values (position_stack).
## Warning: Removed 14 rows containing missing values (position_stack).
## Warning: Removed 14 rows containing missing values (position_stack).
## Warning: Removed 5 rows containing missing values (position_stack).